Papers Abstractive Text Summarization
“Abstractive Text Summarization” 태그가 달린 논문 852편 · 필터 해제
Factorizing Content and Budget Decisions in Abstractive Summarization of Long Documents
We argue that disentangling content selection from the budget used to cover salient content improves the performance and applicability of abstractive summarizers. Our method, FactorSum, does this disentanglement by facto…
Abstractive Text SummarizationDisentanglementDocument SummarizationDomain Adaptation+1Leveraging Locality in Abstractive Text Summarization
Neural attention models have achieved significant improvements on many natural language processing tasks. However, the quadratic memory complexity of the self-attention module with respect to the input length hinders the…
Abstractive Text SummarizationText GenerationText SummarizationCounterfactual Data Augmentation improves Factuality of Abstractive Summarization
Abstractive summarization systems based on pretrained language models often generate coherent but factually inconsistent sentences. In this paper, we present a counterfactual data augmentation approach where we augment d…
Abstractive Text SummarizationcounterfactualData AugmentationDiversityLossless Acceleration for Seq2seq Generation with Aggressive Decoding
We study lossless acceleration for seq2seq generation with a novel decoding algorithm -- Aggressive Decoding. Unlike the previous efforts (e.g., non-autoregressive decoding) speeding up seq2seq generation at the cost of …
Abstractive Text SummarizationGPUGrammatical Error CorrectionMachine Translation+3FactPEGASUS: Factuality-Aware Pre-training and Fine-tuning for Abstractive Summarization
We present FactPEGASUS, an abstractive summarization model that addresses the problem of factuality during pre-training and fine-tuning: (1) We augment the sentence selection strategy of PEGASUS's (Zhang et al., 2020) pr…
Abstractive Text SummarizationContrastive LearningSentenceViT5: Pretrained Text-to-Text Transformer for Vietnamese Language Generation
We present ViT5, a pretrained Transformer-based encoder-decoder model for the Vietnamese language. With T5-style self-supervised pretraining, ViT5 is trained on a large corpus of high-quality and diverse Vietnamese texts…
Abstractive Text SummarizationDecodernamed-entity-recognitionNamed Entity Recognition+4Falsesum: Generating Document-level NLI Examples for Recognizing Factual Inconsistency in Summarization
Neural abstractive summarization models are prone to generate summaries which are factually inconsistent with their source documents. Previous work has introduced the task of recognizing such factual inconsistency as a d…
Abstractive Text SummarizationNatural Language InferenceText GenerationFew-shot Mining of Naturally Occurring Inputs and Outputs
Creating labeled natural language training data is expensive and requires significant human effort. We mine input output examples from large corpora using a supervised mining function trained using a small seed set of on…
Abstractive Text SummarizationData AugmentationReading ComprehensionEfficient Few-Shot Fine-Tuning for Opinion Summarization
Abstractive summarization models are typically pre-trained on large amounts of generic texts, then fine-tuned on tens or hundreds of thousands of annotated samples. However, in opinion summarization, large annotated data…
Abstractive Text SummarizationOpinion SummarizationMasked Summarization to Generate Factually Inconsistent Summaries for Improved Factual Consistency Checking
Despite the recent advances in abstractive summarization systems, it is still difficult to determine whether a generated summary is factual consistent with the source text. To this end, the latest approach is to train a …
Abstractive Text SummarizationLarge-Scale Multi-Document Summarization with Information Extraction and Compression
We develop an abstractive summarization framework independent of labeled data for multiple heterogeneous documents. Unlike existing multi-document summarization methods, our framework processes documents telling differen…
Abstractive Text SummarizationDocument SummarizationLanguage ModelingLanguage Modelling+3FilipN@LT-EDI-ACL2022-Detecting signs of Depression from Social Media: Examining the use of summarization methods as data augmentation for text classification
Depression is a common mental disorder that severely affects the quality of life, and can lead to suicide. When diagnosed in time, mild, moderate, and even severe depression can be treated. This is why it is vital to det…
Abstractive Text SummarizationClassificationData AugmentationDepression Detection+2Meta-X_{NLG}: A Meta-Learning Approach Based on Language Clustering for Zero-Shot Cross-Lingual Transfer and Generation
Recently, the NLP community has witnessed a rapid advancement in multilingual and cross-lingual transfer research where the supervision is transferred from high-resource languages (HRLs) to low-resource languages (LRLs).…
Abstractive Text SummarizationCross-Lingual TransferMeta-LearningQuestion Generation+3NLPOP: a Dataset for Popularity Prediction of Promoted NLP Research on Twitter
Twitter has slowly but surely established itself as a forum for disseminating, analysing and promoting NLP research. The trend of researchers promoting work not yet peer-reviewed (preprints) by posting concise summaries …
Abstractive Text SummarizationCitation RecommendationTowards Making the Most of Cross-Lingual Transfer for Zero-Shot Neural Machine Translation
This paper demonstrates that multilingual pretraining and multilingual fine-tuning are both critical for facilitating cross-lingual transfer in zero-shot translation, where the neural machine translation (NMT) model is t…
Abstractive Text SummarizationCross-Lingual Abstractive SummarizationCross-Lingual TransferDecoder+4Length Control in Abstractive Summarization by Pretraining Information Selection
Previous length-controllable summarization models mostly control lengths at the decoding stage, whereas the encoding or the selection of information from the source document is not sensitive to the designed length. They …
Abstractive Text SummarizationRNSum: A Large-Scale Dataset for Automatic Release Note Generation via Commit Logs Summarization
A release note is a technical document that describes the latest changes to a software product and is crucial in open source software development. However, it still remains challenging to generate release notes automatic…
Abstractive Text SummarizationTwo New Datasets for Italian-Language Abstractive Text Summarization
Text summarization aims to produce a short summary containing relevant parts from a given text. Due to the lack of data for abstractive summarization on low-resource languages such as Italian, we propose two new original…
Abstractive Text SummarizationArticlesMachine TranslationSentence+3Faithful to the Document or to the World? Mitigating Hallucinations via Entity-linked Knowledge in Abstractive Summarization
Despite recent advances in abstractive summarization, current summarization systems still suffer from content hallucinations where models generate text that is either irrelevant or contradictory to the source document. H…
Abstractive Text SummarizationWorld KnowledgeWikiMulti: a Corpus for Cross-Lingual Summarization
Cross-lingual summarization (CLS) is the task to produce a summary in one particular language for a source document in a different language. We introduce WikiMulti - a new dataset for cross-lingual summarization based on…
Abstractive Text SummarizationArticlesCross-Lingual Abstractive Summarization